In this Section, we consider curious agents that only optimize their self-supervised signal for exploration. This means that we omit any external rewards, by setting \(\eta _e = 0\)  (see Background). This focuses the agents solely on the exploratory behaviors inspired by the curiosity mechanisms. For all tasks, we update the policy using the Proximal Policy Optimization algorithm (PPO; [1]}). For all model's components, we use neural networks. For the model's latent stochastic variable, we use distributional layers implemented as linear layers that output the means and standard deviations of a multivariate gaussian.
